A single paper is like a snapshot of the state of research at one
point in time. Research itself is a dynamic, changing, live stream of
results and ideas, and a single snapshot often cannot do justice to
the richness of the topic. The RAM paper is an excellent summary of
definitions and problems, and worth reading today, but, at the time,
it seemed to me more like a crystallization of concepts that were
"in the air" and a clear and precise summary of important
known questions rather than trailblazing exposition of new ideas. The
theory of RAMs is fascinating, and I'll try to summarize some of the
relevant work that preceded Cook's.

The RAM formulation dates back to von Neumann (after all the "von
Neumann architecture" IS a RAM). von Neumann uses the RAM
formulation to derive instruction counts for some programs for his
first computer. So "unit cost RAMs" were well known from the
beginning of computers, and counting the number of operations was
known to be important. Knuth was a very strong advocate of the idea of
analyzing the running time of algorithms using instruction counts: the
first edition of the first volume of The Art of Computer Programming
is from 1968.

Theoreticians interested in computability theory have published
extensively on RAMs: an example of an early paper is Sheperdson and
Sturgis JACM 1963. It has a bibliography of earlier work. These papers
came from two different motivations: one was to find further examples
of formalisms equivalent to Turing machines, as a kind of experimental
evidence for Church's Thesis (see the book by
Brainerd and Landweber for an exposition of a few dozen
formalisms—Markov Algorithms, Post Systems, λ-calculus, and so
on). The other was to find "better", more realistic
theoretical models for real computers.

For example, one of the novel features of the ENIAC was that the
program actually resided in the computer's memory (as opposed to
outside fixed set of instructions as in the earlier Harvard Mark
machines). Much was made of this feature of "stored program"
that allows for the program to use itself as data and modify itself on
the run, something that was judged to be "good for AI." Of
course, the existence of a two-state universal Turing machine is a
clear illustration that at a fundamental level of computability there
is no difference between "hardware" and
"software". Still, there was a great deal of interest to
model such "ease of programming" features at a theoretical
level. For example, Juris Hartmanis has an elegant result showing
that there is a function that can be computed faster on a RASP (random
access stored program machine) than on a RAM (Math Systems Theory,
1971).

So "RAM complexity" was alive and well. What made things
confusing was that fixed length register RAMs are uninteresting, but
if one allows unbounded length registers, it is unclear whether unit
cost is a realistic measure, and, if not, what would be reasonable. A
natural option is to charge for the length of the register that is
effectively used, the log of the value stored. Of course, there is the
problem that determining the complexity of an algorithm becomes even
harder. Even peskier questions appear if one asks whether addition and
multiplication should have the same cost, and if not, should one use
the schoolbook (quadratic) cost, or perhaps the Sconhage-Strassen
cost? Most researchers opted to use the unit cost, and avoid all these
complications.

To make things worse, many algorithms in optimization are expressed
naturally in terms RAMs with real numbers registers. Note that
fundamental questions about this latter model are still very much
open.

To summarize, measuring number of RAM steps as a complexity measure
was not a novel idea. What made the Cook paper relevant was exactly
the proliferation of RAM measure results. In particular the
Stockmeyer-Pratt-Rabin vector machine paper (and the later
Hartmanis-Simon multiplication RAMs) as well as RAMs holding reals
used in the OR community made it important to be precise about the
exact meaning of "number of RAM instructions" measure. The
community was quite aware that logarithmic cost was polynomially
equivalent to Turing machines, and these papers showed that unit cost
likely wasn't. Cook and Reckhow wrote down precisely what was likely
a kind of unwritten consensus among the researchers in the area. This
was necessary and useful, but it did not "set the stage to make
RAMs the gold standard". The stage was already set, people were
using RAMs to analyze algorithms, and Cook and Reckhow was a precise
and meticulous description of what this meant.

In short, if one wants a picture of what great things got started in
the period, the paper is an excellent choice, but, as usual, the
actual history is complicated, dynamic, and, I hope, interesting.